{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predictor Training\n",
    "==============\n",
    "\n",
    "To maximize its accuracy, the yield predictor is periodically re-trained on encoder output during encoder training. However, it is beneficial to seed the predictor by training it on random sequences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import cupyck\n",
    "import primo.models\n",
    "import primo.tools.sequences as seqtools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize the cupyck session first, otherwise Tensorflow will use up all available GPU memory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "cupyck_sess = cupyck.GPUSession(max_seqlen=200, nblocks=1024, nthreads=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "simulator = primo.models.Simulator(cupyck_sess)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate a large set of pairs with various Hamming distances, then simulate their query-target yields with NUPACK:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate two random DNA sequences. First by creating a random sequence, then copying that sequence to another sequence, then mutating that second sequence at some given mutation rate, such that \n",
    "# the hamming distance between the sequences (i.e. how different they are) is sampled from a uniform distribution. This means that we could expect sequence that are nearly identical, and sequence pairs that are entirely opposite to occur at approximately the same rate. \n",
    "# Random_pairs: (arbitrary_sequence_1, arbitrary_sequence_2)\n",
    "\n",
    "# Generate 5,000 of these arbitrary sequence pairs\n",
    "# ...of length 80 nucleotides each (80 was chosen because that's the length of the DNA sequence's feature region in callie original paper).\n",
    "random_pairs, mut_rates = seqtools.random_mutant_pairs(5000, 80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# From here, we take the first arbitrary sequence, and its mutation (i.e. the random pairs), then we pretend that the first arbitrary sequence (i.e. at index 0) is a target DNA sequence, then we pretend the second arbitrary sequence (i.e. at index 1) is a query DNA sequences. \n",
    "seq_pairs = pd.DataFrame({\n",
    "    \"target_features\": random_pairs[:, 0],\n",
    "    \"query_features\": random_pairs[:, 1]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use NUPACK/CUPYCK to simulate the hybdrization yields of the two sequences in the pair (this is the Pink flow in the above diagram). \n",
    "sim_results = simulator.simulate(seq_pairs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize a random predictor and train it on the sequences and simulated yields:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling __init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n"
     ]
    }
   ],
   "source": [
    "predictor = primo.models.Predictor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "onehot_seq_pairs = predictor.seq_pairs_to_onehots(seq_pairs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "Train on 4000 samples, validate on 1000 samples\n",
      "Epoch 1/50\n",
      "4000/4000 [==============================] - 7s 2ms/sample - loss: 0.3867 - val_loss: 0.2730\n",
      "Epoch 2/50\n",
      "4000/4000 [==============================] - 3s 780us/sample - loss: 0.2304 - val_loss: 0.2357\n",
      "Epoch 3/50\n",
      "4000/4000 [==============================] - 3s 783us/sample - loss: 0.2133 - val_loss: 0.2302\n",
      "Epoch 4/50\n",
      "4000/4000 [==============================] - 3s 783us/sample - loss: 0.2098 - val_loss: 0.2289\n",
      "Epoch 5/50\n",
      "4000/4000 [==============================] - 3s 781us/sample - loss: 0.2081 - val_loss: 0.2280\n",
      "Epoch 6/50\n",
      "4000/4000 [==============================] - 3s 782us/sample - loss: 0.2060 - val_loss: 0.2294\n",
      "Epoch 7/50\n",
      "4000/4000 [==============================] - 3s 780us/sample - loss: 0.2049 - val_loss: 0.2261\n",
      "Epoch 8/50\n",
      "4000/4000 [==============================] - 3s 782us/sample - loss: 0.2041 - val_loss: 0.2259\n",
      "Epoch 9/50\n",
      "4000/4000 [==============================] - 3s 777us/sample - loss: 0.2027 - val_loss: 0.2243\n",
      "Epoch 10/50\n",
      "4000/4000 [==============================] - 3s 780us/sample - loss: 0.2015 - val_loss: 0.2236\n",
      "Epoch 11/50\n",
      "4000/4000 [==============================] - 3s 790us/sample - loss: 0.2004 - val_loss: 0.2224\n",
      "Epoch 12/50\n",
      "4000/4000 [==============================] - 3s 782us/sample - loss: 0.2002 - val_loss: 0.2216\n",
      "Epoch 13/50\n",
      "4000/4000 [==============================] - 3s 782us/sample - loss: 0.1981 - val_loss: 0.2285\n",
      "Epoch 14/50\n",
      "4000/4000 [==============================] - 3s 780us/sample - loss: 0.1979 - val_loss: 0.2207\n",
      "Epoch 15/50\n",
      "4000/4000 [==============================] - 3s 784us/sample - loss: 0.1967 - val_loss: 0.2220\n",
      "Epoch 16/50\n",
      "4000/4000 [==============================] - 3s 782us/sample - loss: 0.1967 - val_loss: 0.2192\n",
      "Epoch 17/50\n",
      "4000/4000 [==============================] - 3s 784us/sample - loss: 0.1957 - val_loss: 0.2213\n",
      "Epoch 18/50\n",
      "4000/4000 [==============================] - 3s 782us/sample - loss: 0.1945 - val_loss: 0.2178\n",
      "Epoch 19/50\n",
      "4000/4000 [==============================] - 3s 782us/sample - loss: 0.1944 - val_loss: 0.2215\n",
      "Epoch 20/50\n",
      "4000/4000 [==============================] - 3s 791us/sample - loss: 0.1936 - val_loss: 0.2201\n",
      "Epoch 21/50\n",
      "4000/4000 [==============================] - 3s 785us/sample - loss: 0.1929 - val_loss: 0.2166\n",
      "Epoch 22/50\n",
      "4000/4000 [==============================] - 3s 787us/sample - loss: 0.1928 - val_loss: 0.2165\n",
      "Epoch 23/50\n",
      "4000/4000 [==============================] - 3s 787us/sample - loss: 0.1923 - val_loss: 0.2156\n",
      "Epoch 24/50\n",
      "4000/4000 [==============================] - 3s 783us/sample - loss: 0.1917 - val_loss: 0.2149\n",
      "Epoch 25/50\n",
      "4000/4000 [==============================] - 3s 786us/sample - loss: 0.1917 - val_loss: 0.2143\n",
      "Epoch 26/50\n",
      "4000/4000 [==============================] - 3s 780us/sample - loss: 0.1900 - val_loss: 0.2171\n",
      "Epoch 27/50\n",
      "4000/4000 [==============================] - 3s 779us/sample - loss: 0.1891 - val_loss: 0.2158\n",
      "Epoch 28/50\n",
      "4000/4000 [==============================] - 3s 786us/sample - loss: 0.1904 - val_loss: 0.2176\n",
      "Epoch 29/50\n",
      "4000/4000 [==============================] - 3s 779us/sample - loss: 0.1889 - val_loss: 0.2153\n",
      "Epoch 30/50\n",
      "4000/4000 [==============================] - 3s 786us/sample - loss: 0.1888 - val_loss: 0.2147\n",
      "Epoch 31/50\n",
      "4000/4000 [==============================] - 3s 776us/sample - loss: 0.1886 - val_loss: 0.2127\n",
      "Epoch 32/50\n",
      "4000/4000 [==============================] - 3s 778us/sample - loss: 0.1886 - val_loss: 0.2139\n",
      "Epoch 33/50\n",
      "4000/4000 [==============================] - 3s 783us/sample - loss: 0.1872 - val_loss: 0.2189\n",
      "Epoch 34/50\n",
      "4000/4000 [==============================] - 3s 785us/sample - loss: 0.1876 - val_loss: 0.2149\n",
      "Epoch 35/50\n",
      "4000/4000 [==============================] - 3s 783us/sample - loss: 0.1874 - val_loss: 0.2126\n",
      "Epoch 36/50\n",
      "4000/4000 [==============================] - 3s 785us/sample - loss: 0.1865 - val_loss: 0.2130\n",
      "Epoch 37/50\n",
      "4000/4000 [==============================] - 3s 787us/sample - loss: 0.1867 - val_loss: 0.2108\n",
      "Epoch 38/50\n",
      "4000/4000 [==============================] - 3s 779us/sample - loss: 0.1867 - val_loss: 0.2122\n",
      "Epoch 39/50\n",
      "4000/4000 [==============================] - 3s 780us/sample - loss: 0.1859 - val_loss: 0.2143\n",
      "Epoch 40/50\n",
      "4000/4000 [==============================] - 3s 788us/sample - loss: 0.1862 - val_loss: 0.2170\n",
      "Epoch 41/50\n",
      "4000/4000 [==============================] - 3s 784us/sample - loss: 0.1865 - val_loss: 0.2118\n",
      "Epoch 42/50\n",
      "4000/4000 [==============================] - 3s 788us/sample - loss: 0.1863 - val_loss: 0.2135\n",
      "Epoch 43/50\n",
      "4000/4000 [==============================] - 3s 777us/sample - loss: 0.1858 - val_loss: 0.2106\n",
      "Epoch 44/50\n",
      "4000/4000 [==============================] - 3s 788us/sample - loss: 0.1859 - val_loss: 0.2104\n",
      "Epoch 45/50\n",
      "4000/4000 [==============================] - 3s 786us/sample - loss: 0.1851 - val_loss: 0.2131\n",
      "Epoch 46/50\n",
      "4000/4000 [==============================] - 3s 785us/sample - loss: 0.1856 - val_loss: 0.2110\n",
      "Epoch 47/50\n",
      "4000/4000 [==============================] - 3s 779us/sample - loss: 0.1854 - val_loss: 0.2114\n",
      "Epoch 48/50\n",
      "4000/4000 [==============================] - 3s 781us/sample - loss: 0.1849 - val_loss: 0.2102\n",
      "Epoch 49/50\n",
      "4000/4000 [==============================] - 3s 784us/sample - loss: 0.1843 - val_loss: 0.2122\n",
      "Epoch 50/50\n",
      "4000/4000 [==============================] - 3s 778us/sample - loss: 0.1858 - val_loss: 0.2115\n"
     ]
    }
   ],
   "source": [
    "history = predictor.train(onehot_seq_pairs, sim_results.duplex_yield, epochs=50, validation_split = 0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the model's accuracy by plotting the simulated yield against the pairwise Hamming distance, and coloring by the difference between the predicted yield and the simulated yield. The predictor should be most accurate along an S-shaped region in the center."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_yield = predictor.model.predict(onehot_seq_pairs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f67a00ad610>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(mut_rates, sim_results.duplex_yield, c = np.abs(pred_yield.flatten() - sim_results.duplex_yield))\n",
    "plt.xlabel(\"Hamming distance\")\n",
    "plt.ylabel(\"Simulated yield\")\n",
    "plt.colorbar(label=\"|Simulated - Predicted|\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the predictor for future training and use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor.save('/tf/primo/data/models/yield-predictor.h5')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15+"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}